“ClusterApp”: A Shiny R application to guide cluster studies based on GPS data
Abstract The rapid evolution of GPS devices, and therefore, collection of GPS data can be used to investigate a wide variety of topics in wildlife research. The combination of remotely collected GPS data with on‐the‐ground field investigations is a powerful tool for exploring behavioral ecology. “GP...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-07-01
|
| Series: | Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ece3.11695 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849420610345631744 |
|---|---|
| author | Johanna Heeres Aimee Tallian Camilla Wikenros Rick W. Heeres |
| author_facet | Johanna Heeres Aimee Tallian Camilla Wikenros Rick W. Heeres |
| author_sort | Johanna Heeres |
| collection | DOAJ |
| description | Abstract The rapid evolution of GPS devices, and therefore, collection of GPS data can be used to investigate a wide variety of topics in wildlife research. The combination of remotely collected GPS data with on‐the‐ground field investigations is a powerful tool for exploring behavioral ecology. “GPS cluster studies” are aimed at pinpointing and investigating identified clusters in the field. Activity clusters can be based on various parameters (e.g., distance between GPS locations and the number of locations needed to establish a cluster), which are closely related to the set research questions. Variation in methods across years within the same study may result in data collection biases. Therefore, a streamlined method to parametrize, generate interactive maps, and extract activity cluster data using a predefined approach will limit biases, and make field work and data management straightforward for field technicians. We developed the “ClusterApp” Shiny application in the R software to facilitate a step‐by‐step guide to execute cluster analyses and data management of cluster studies on any species using GPS data. We illustrate the use of the “ClusterApp” with two location datasets constructed by data collected on brown bears (Ursus arctos) and gray wolves (Canis lupus). |
| format | Article |
| id | doaj-art-e02bc90c787a4ff89f2d308ee9e901ae |
| institution | Kabale University |
| issn | 2045-7758 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj-art-e02bc90c787a4ff89f2d308ee9e901ae2025-08-20T03:31:42ZengWileyEcology and Evolution2045-77582024-07-01147n/an/a10.1002/ece3.11695“ClusterApp”: A Shiny R application to guide cluster studies based on GPS dataJohanna Heeres0Aimee Tallian1Camilla Wikenros2Rick W. Heeres3Department of Ecology Swedish University of Agricultural Sciences Riddarhyttan SwedenNorwegian Institute for Nature Research Trondheim NorwayDepartment of Ecology Swedish University of Agricultural Sciences Riddarhyttan SwedenDepartment of Natural Sciences and Environmental Health University of South‐Eastern Norway Bø NorwayAbstract The rapid evolution of GPS devices, and therefore, collection of GPS data can be used to investigate a wide variety of topics in wildlife research. The combination of remotely collected GPS data with on‐the‐ground field investigations is a powerful tool for exploring behavioral ecology. “GPS cluster studies” are aimed at pinpointing and investigating identified clusters in the field. Activity clusters can be based on various parameters (e.g., distance between GPS locations and the number of locations needed to establish a cluster), which are closely related to the set research questions. Variation in methods across years within the same study may result in data collection biases. Therefore, a streamlined method to parametrize, generate interactive maps, and extract activity cluster data using a predefined approach will limit biases, and make field work and data management straightforward for field technicians. We developed the “ClusterApp” Shiny application in the R software to facilitate a step‐by‐step guide to execute cluster analyses and data management of cluster studies on any species using GPS data. We illustrate the use of the “ClusterApp” with two location datasets constructed by data collected on brown bears (Ursus arctos) and gray wolves (Canis lupus).https://doi.org/10.1002/ece3.11695animal activitycluster analysisfieldworkmovement dataShiny application |
| spellingShingle | Johanna Heeres Aimee Tallian Camilla Wikenros Rick W. Heeres “ClusterApp”: A Shiny R application to guide cluster studies based on GPS data Ecology and Evolution animal activity cluster analysis fieldwork movement data Shiny application |
| title | “ClusterApp”: A Shiny R application to guide cluster studies based on GPS data |
| title_full | “ClusterApp”: A Shiny R application to guide cluster studies based on GPS data |
| title_fullStr | “ClusterApp”: A Shiny R application to guide cluster studies based on GPS data |
| title_full_unstemmed | “ClusterApp”: A Shiny R application to guide cluster studies based on GPS data |
| title_short | “ClusterApp”: A Shiny R application to guide cluster studies based on GPS data |
| title_sort | clusterapp a shiny r application to guide cluster studies based on gps data |
| topic | animal activity cluster analysis fieldwork movement data Shiny application |
| url | https://doi.org/10.1002/ece3.11695 |
| work_keys_str_mv | AT johannaheeres clusterappashinyrapplicationtoguideclusterstudiesbasedongpsdata AT aimeetallian clusterappashinyrapplicationtoguideclusterstudiesbasedongpsdata AT camillawikenros clusterappashinyrapplicationtoguideclusterstudiesbasedongpsdata AT rickwheeres clusterappashinyrapplicationtoguideclusterstudiesbasedongpsdata |